Visibility Recovery on Images Acquired in Attenuating Media. Application to Underwater, Fog, and Mammographic Imaging
When acquired in attenuating media, digital images often suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasantness for the user. In these cases, mathematical image...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | TECNALIA Research & Innovation |
| Repositorio: | TECNALIA Publications |
| Idioma: | inglés |
| OAI Identifier: | oai:dsp.tecnalia.com:11556/199 |
| Acceso en línea: | http://hdl.handle.net/11556/199 |
| Access Level: | acceso abierto |
| Palabra clave: | Contrast Enhancement Visibility Improvement Image Dehazing Underwater Image Enhancement Color Correction Medical Imaging |
| Sumario: | When acquired in attenuating media, digital images often suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasantness for the user. In these cases, mathematical image processing reveals itself as an ideal tool to recover some of the information lost during the degradation process. In this dissertation, we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fog removal and mammographic image processing. In the case of digital mammograms, X-ray beams traverse human tissue, and electronic detectors capture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces lowcontrasted images in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility. For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges, in this dissertation we develop new methodologies that rely on: a) physical models of the observed degradation, and b) the calculus of variations. Equipped with this powerful machinery, we design novel theoretical and computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energies are composed of different integral terms that are simultaneously minimized by means of efficient numerical schemes, producing a clean, visually-pleasant and useful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validate our methods, confirming that the developed techniques outperform other existing approaches in the literature. |
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